> For the complete documentation index, see [llms.txt](https://docs.ta-da.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.ta-da.io/ta-da-platform/architecture/quality-control.md). # Quality Control ### Verification flow Quality controls are made at two levels: * task level: making sure each task has been properly done * job level: making sure all tasks have been completed consistently with requirements Our quality check relies upon 3 levels of validation, both fully customizable based on clients' needs: * Consensus based verification: verification is done by "community verifiers", and validation is made based on consensus, with "slashing" of incorrect productions or colluding verifiers to incentivize good behaviours * AI-powered review: specifically trained LLMs help screen compliant tasks to increase verification throughput * Curator validation: verification is done by specifically trained curators Depending upon clients' needs and projects' complexity, it is possible to combine any of these 3 verification mechanisms to provide stronger quality checks Job validation triggers the distribution of rewards, unless specified otherwise in instructions ### **QC core principles** Ta-da's platform is build so as to enable quality controls at scale, guaranteeing better data quality, at * **Redundancy models** (e.g., consensus, majority voting) can be expensive and slow. * **Automated quality checks** often struggle with edge cases or subtle errors. * **Data injection** and real-time feedback loops * **Integration with ML pipelines** or client APIs to close the loop between data needs and data supply. * **Analytics dashboards** to help data consumers measure dataset evolution, annotation variance, and coverage gaps. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.ta-da.io/ta-da-platform/architecture/quality-control.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.